metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >+
Dear [Recipient],
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email/phone number].
Thank you for your attention to this matter.
Best regards,
[Your Name]
[Your Position/Department (if applicable)]
[Your Company/Organization Name]
- text: >-
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[Your Position/Department (if applicable)]
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pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 10 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| Password reset emails |
|
| System Alerts |
|
| Promotional emails |
|
| Automatic subscription confirmation emails |
|
| Email Delivery failure notifications |
|
| Out-of- office replies |
|
| Read receipts or delivery confirmation |
|
| Do-not-reply or no-reply emails (explicitly mentioned in email) |
|
| Reminders |
|
| Security Alerts |
|
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mohamednihal/noReply")
# Run inference
preds = model("Hello,
Thank you for your email. I am currently attending a conference and may have limited access to email until [Date of Return]. I will respond to your message as soon as possible upon my return. For urgent matters, please contact [Alternative Contact Information].
Best regards,
[Your Name]")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 44 | 85.24 | 170 |
| Label | Training Sample Count |
|---|---|
| Automatic subscription confirmation emails | 5 |
| Do-not-reply or no-reply emails (explicitly mentioned in email) | 5 |
| Email Delivery failure notifications | 5 |
| Out-of- office replies | 5 |
| Password reset emails | 5 |
| Promotional emails | 5 |
| Read receipts or delivery confirmation | 5 |
| Reminders | 5 |
| Security Alerts | 5 |
| System Alerts | 5 |
Training Hyperparameters
- batch_size: (2, 2)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2.887681170264626e-05, 2.887681170264626e-05)
- head_learning_rate: 2.887681170264626e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.001 | 1 | 0.1811 | - |
| 0.05 | 50 | 0.3964 | - |
| 0.1 | 100 | 0.0705 | - |
| 0.15 | 150 | 0.0115 | - |
| 0.2 | 200 | 0.0477 | - |
| 0.25 | 250 | 0.0022 | - |
| 0.3 | 300 | 0.0044 | - |
| 0.35 | 350 | 0.0017 | - |
| 0.4 | 400 | 0.001 | - |
| 0.45 | 450 | 0.0001 | - |
| 0.5 | 500 | 0.0006 | - |
| 0.55 | 550 | 0.0008 | - |
| 0.6 | 600 | 0.0003 | - |
| 0.65 | 650 | 0.0006 | - |
| 0.7 | 700 | 0.0003 | - |
| 0.75 | 750 | 0.0017 | - |
| 0.8 | 800 | 0.0001 | - |
| 0.85 | 850 | 0.0002 | - |
| 0.9 | 900 | 0.0 | - |
| 0.95 | 950 | 0.0002 | - |
| 1.0 | 1000 | 0.0002 | - |
| 0.001 | 1 | 0.0001 | - |
| 0.05 | 50 | 0.0002 | - |
| 0.1 | 100 | 0.0014 | - |
| 0.15 | 150 | 0.008 | - |
| 0.2 | 200 | 0.0017 | - |
| 0.25 | 250 | 0.0018 | - |
| 0.3 | 300 | 0.0187 | - |
| 0.35 | 350 | 0.0021 | - |
| 0.4 | 400 | 0.0001 | - |
| 0.45 | 450 | 0.0 | - |
| 0.5 | 500 | 0.0003 | - |
| 0.55 | 550 | 0.0001 | - |
| 0.6 | 600 | 0.0 | - |
| 0.65 | 650 | 0.0002 | - |
| 0.7 | 700 | 0.0 | - |
| 0.75 | 750 | 0.0003 | - |
| 0.8 | 800 | 0.0 | - |
| 0.85 | 850 | 0.0 | - |
| 0.9 | 900 | 0.0001 | - |
| 0.95 | 950 | 0.0001 | - |
| 1.0 | 1000 | 0.0 | - |
| 0.001 | 1 | 0.0 | - |
| 0.05 | 50 | 0.0001 | - |
| 0.1 | 100 | 0.0018 | - |
| 0.15 | 150 | 0.0001 | - |
| 0.2 | 200 | 0.0042 | - |
| 0.25 | 250 | 0.0009 | - |
| 0.3 | 300 | 0.0001 | - |
| 0.35 | 350 | 0.0018 | - |
| 0.4 | 400 | 0.0002 | - |
| 0.45 | 450 | 0.0001 | - |
| 0.5 | 500 | 0.0 | - |
| 0.55 | 550 | 0.0001 | - |
| 0.6 | 600 | 0.0 | - |
| 0.65 | 650 | 0.0 | - |
| 0.7 | 700 | 0.0 | - |
| 0.75 | 750 | 0.0 | - |
| 0.8 | 800 | 0.0 | - |
| 0.85 | 850 | 0.0 | - |
| 0.9 | 900 | 0.0 | - |
| 0.95 | 950 | 0.0 | - |
| 1.0 | 1000 | 0.0 | - |
| 0.001 | 1 | 0.0 | - |
| 0.05 | 50 | 0.0 | - |
| 0.1 | 100 | 0.0005 | - |
| 0.15 | 150 | 0.0025 | - |
| 0.2 | 200 | 0.0 | - |
| 0.25 | 250 | 0.0002 | - |
| 0.3 | 300 | 0.0 | - |
| 0.35 | 350 | 0.0003 | - |
| 0.4 | 400 | 0.0001 | - |
| 0.45 | 450 | 0.0 | - |
| 0.5 | 500 | 0.0 | - |
| 0.55 | 550 | 0.0 | - |
| 0.6 | 600 | 0.0 | - |
| 0.65 | 650 | 0.0 | - |
| 0.7 | 700 | 0.0 | - |
| 0.75 | 750 | 0.0 | - |
| 0.8 | 800 | 0.0 | - |
| 0.85 | 850 | 0.0 | - |
| 0.9 | 900 | 0.0 | - |
| 0.95 | 950 | 0.0 | - |
| 1.0 | 1000 | 0.0 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.4.0
- Transformers: 4.37.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.1
- Tokenizers: 0.15.2
Citation
BibTeX
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}